MirMark: a site-level and UTR-level classifier for miRNA target prediction.
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ABSTRACT:
SUBMITTER: Menor M
PROVIDER: S-EPMC4243195 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
Publications
mirMark: a site-level and UTR-level classifier for miRNA target prediction.
Menor Mark M Ching Travers T Zhu Xun X Garmire David D Garmire Lana X LX
Genome biology 20140101 10
MiRNAs play important roles in many diseases including cancers. However computational prediction of miRNA target genes is challenging and the accuracies of existing methods remain poor. We report mirMark, a new machine learning-based method of miRNA target prediction at the site and UTR levels. This method uses experimentally verified miRNA targets from miRecords and mirTarBase as training sets and considers over 700 features. By combining Correlation-based Feature Selection with a variety of st ...[more]
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Project description:MotivationIt is a fundamental task to identify microRNAs (miRNAs) targets and accurately locate their target sites. Genome-scale experiments for miRNA target site detection are still costly. The prediction accuracies of existing computational algorithms and tools are often not up to the expectation due to a large number of false positives. One major obstacle to achieve a higher accuracy is the lack of knowledge of the target binding features of miRNAs. The published high-throughput experimental data provide an opportunity to analyze position-wise preference of miRNAs in terms of target binding, which can be an important feature in miRNA target prediction algorithms.ResultsWe developed a Markov model to characterize position-wise pairing patterns of miRNA-target interactions. We further integrated this model as a scoring method and developed a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for miRNA target site Selection) that can screen putative target sites of miRNA-target binding. The MDPS algorithm thus can take into account both the dependency of neighboring pairing positions and the global pairing information. Based on the trained Markov models from both miRNA-specific and general datasets, we discovered that the position-wise binding information specific to a given miRNA would benefit its target prediction. We also found that miRNAs maintain region-wise similarity in their target binding patterns. Combining MDPS with existing methods significantly improves their precision while only slightly reduces their recall. Therefore, position-wise pairing patterns have the promise to improve target prediction if incorporated into existing software tools.Availability and implementationThe source code and tool to calculate MDPS score is available at http://hulab.ucf.edu/research/projects/MDPS/index.html.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8453239 | biostudies-literature
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| S-EPMC5587804 | biostudies-other
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| S-EPMC1160144 | biostudies-literature
Project description:miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets.
| S-EPMC5376335 | biostudies-literature
Project description:BackgroundmicroRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.ResultsIn this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and the ground truth for validation are available in the Supplementary materials.
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Project description:Single nucleotide polymorphisms (SNPs) can have significant effects on phenotypic characteristics in cattle. MicroRNAs (miRNAs) are small, non-coding RNAs that act as post-transcriptional regulators by binding them to target mRNAs. In the present study, we scanned ~56 million SNPs against 1,064 bovine miRNA sequences and analyzed, in silico, their possible effects on target binding prediction, primary miRNA formation, association with QTL regions and the evolutionary conservation for each SNP locus. Following target prediction, we show that 71.6% of miRNA predicted targets were altered as a consequence of SNPs located within the seed region of the mature miRNAs. Next, we identified variations in the Minimum Free Energy (MFE), which represents the capacity to alter molecule stability and, consequently, miRNA maturation. A total of 48.6% of the sequences analyzed showed values within those previously reported as sufficient to alter miRNA maturation. We have also found 131 SNPs in 46 miRNAs, with altered target prediction, occurring in QTL regions. Lastly, analysis of evolutionary conservation scores for each SNP locus suggested that they have a conserved biological function through the evolutionary process. Our results suggest that SNPs in microRNAs have the potential to affect bovine phenotypes and could be of great value for genetic improvement studies, as well as production.
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Project description:BACKGROUND:MicroRNAs (miRNAs) are approximately 22-nucleotide long regulatory RNA that mediate RNA interference by binding to cognate mRNA target regions. Here, we present a distributed kernel SVM-based binary classification scheme to predict miRNA targets. It captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves. This is accomplished separately for various input features, such as thermodynamic and sequence-based features. Further, we use a principled approach to uniformly model both canonical and non-canonical seed matches, using a novel seed enrichment metric. Finally, we verify our miRNA-mRNA pairings using an Elastic Net-based regression model on TCGA expression data for four cancer types to estimate the miRNAs that together regulate any given mRNA. RESULTS:We present a suite of algorithms for miRNA target prediction, under the banner Avishkar, with superior prediction performance over the competition. Specifically, our final kernel SVM model, with an Apache Spark backend, achieves an average true positive rate (TPR) of more than 75 percent, when keeping the false positive rate of 20 percent, for non-canonical human miRNA target sites. This is an improvement of over 150 percent in the TPR for non-canonical sites, over the best-in-class algorithm. We are able to achieve such superior performance by representing the thermodynamic and sequence profiles of miRNA-mRNA interaction as curves, devising a novel seed enrichment metric, and learning an ensemble of miRNA family-specific kernel SVM classifiers. We provide an easy-to-use system for large-scale interactive analysis and prediction of miRNA targets. All operations in our system, namely candidate set generation, feature generation and transformation, training, prediction, and computing performance metrics are fully distributed and are scalable. CONCLUSIONS:We have developed an efficient SVM-based model for miRNA target prediction using recent CLIP-seq data, demonstrating superior performance, evaluated using ROC curves for different species (human or mouse), or different target types (canonical or non-canonical). We analyzed the agreement between the target pairings using CLIP-seq data and using expression data from four cancer types. To the best of our knowledge, we provide the first distributed framework for miRNA target prediction based on Apache Hadoop and Spark. AVAILABILITY:All source code and sample data are publicly available at https://bitbucket.org/cellsandmachines/avishkar. Our scalable implementation of kernel SVM using Apache Spark, which can be used to solve large-scale non-linear binary classification problems, is available at https://bitbucket.org/cellsandmachines/kernelsvmspark.
| S-EPMC6175706 | biostudies-literature
Project description:MicroRNAs often occur in families whose members share an identical 5' terminal 'seed' sequence. The seed is a major determinant of miRNA activity, and family members are thought to act redundantly on target mRNAs with perfect seed matches, i.e. sequences complementary to the seed. However, recently sequences outside the seed were reported to promote silencing by individual miRNA family members. Here, we examine this concept and the importance of miRNA specificity for the robustness of developmental gene control. Using the let-7 miRNA family in Caenorhabditis elegans, we find that seed match imperfections can increase specificity by requiring extensive pairing outside the miRNA seed region for efficient silencing and that such specificity is needed for faithful worm development. In addition, for some target site architectures, elevated miRNA levels can compensate for a lack of complementarity outside the seed. Thus, some target sites require higher miRNA concentration for silencing than others, contrasting with a traditional binary distinction between functional and non-functional sites. We conclude that changing miRNA concentrations can alter cellular miRNA target repertoires. This diversifies possible biological outcomes of miRNA-mediated gene regulation and stresses the importance of target validation under physiological conditions to understand miRNA functions in vivo.
| S-EPMC5909448 | biostudies-literature